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An Efficient User Verification System Using Angle-Based Mouse Movement Biometrics

Biometric authentication verifies a user based on its inherent, unique characteristics - who you are. In addition to physiological biometrics, behavioral biometrics has proven very useful in authenticating a user. Mouse dynamics, with their unique patterns of mouse movements, is one such behavioral biometric. In this article, we present a user verification system using mouse dynamics, which is transparent to users and can be naturally applied for continuous reauthentication. The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification. These new metrics are relatively unique from person to person and independent of a computing platform. Moreover, we utilize support vector machines (SVMs) for quick and accurate classification. Our technique is robust across different operating platforms, and no specialized hardware is required. The efficacy of our approach is validated through a series of experiments, which are based on three sets of user mouse movement data collected in controllable environments and in the field. Our experimental results show that the proposed system can verify a user in an accurate and timely manner, with minor induced system overhead.

Source: http://dl.acm.org/citation.cfm?id=2046725

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accurate accurate approach computing data dynamics dynamics environments feature induced inherent minor required series technique unique unique unique utilize validated

1. accurate
• Moreover, we utilize support vector machines (SVMs) for quick and accurate classification.
2. approach
• The efficacy of our approach is validated through a series of experiments
3. computing
• These new metrics are relatively unique from person to person and independent of a computing platform.
4. data
• The efficacy of our approach is validated through a series of experiments, which are based on three sets of user mouse movement data collected in controllable environments and in the field.
5. dynamics
• Mouse dynamics, with their unique patterns of mouse movements, is one such behavioral biometric. 
6. environments
• There are three sets of user mouse movement data collected in controllable environments and in the field.
7. feature
• The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification.
8. minor induced
• Our experimental results show that the proposed system can verify a user in an accurate and timely manner, with minor induced system overhead.
9. inherent
• Biometric authentication verifies a user based on its inherent, unique characteristics - who you are.
10. required
• Our technique is robust across different operating platforms, and no specialized hardware is required.
11. series
• The efficacy of our approach is validated through a series of experiments
12. technique
• Our technique is robust across different operating platforms
13. unique
• Biometric authentication verifies a user based on its inherent, unique characteristics
14. utilize
• We utilize support vector machines (SVMs) for quick and accurate classification.
15. validated
• The efficacy of our approach is validated through a series of experiments